Seema P.

Oversaw comprehensive pharmacovigilance operations including collection of ICSR/SAE, AE coding, causality assessment, reconciliation, case narratives, and database entry in accordance with ICH/GCP/GVP guidelines. Directed efforts in literature safety surveillance and signal detection, crafting SOPs and job aids to ensure compliance during audits and inspections. Applied regulatory guidelines for clinical study setup, execution, and closure; managed clinical databases based on REDCap with GLCV-aligned documentation. Engaged in ADMET profiling, pharmacogenomics, and chemical data curation, including variant annotation and studies on drug response/resistance. Led extensive data curation projects to sustain drug discovery pipelines, validating complex data sets from literature, databases, and clinical trials. Executed variant annotation, evaluated pharmacogenomic evidence, and generated disease synopses consistent with GLCV standards. Annotated drug mechanisms and molecular targets while standardizing relationships with HGNC-aligned protein annotations. Curated comprehensive toxicity, ADMET, pharmacokinetics, and metabolic pathway data from NDA documents for ChEMBL deposition, ensuring schema adherence and quality assurance. Conducted structured classification of clinical trial endpoint results, performing literature reviews and documentation based on evidence. Assessed biological relevance and mechanistic roles of protein targets through integrated pharmacological and literature analysis, while coordinating project activities to support timely dataset delivery and preparing structured reports for leadership. Enhanced workflow efficiency via KNIME automation and scripting, contributing to internal and external audits and meetings.

Utilized biomedical knowledge graphs such as PrimeKG, Robokop, Hetionet, and Neo4j to derive insights for drug repurposing, developing KG-driven tools including DRKG workflows and MLP-based link prediction pipelines. Contributed to machine learning pipelines for predicting drug-target interactions, drug repurposing, and ADMET predictions using Python, and supported pathway activation modeling via iPANDA and Oncobox frameworks. Participated in establishing the Rare Disease Knowledge Platform addressing over 6,000 rare diseases, aimed at enhancing research and clinical decision-making. Designed interactive Streamlit dashboards and FastAPI frontends to facilitate data visualization, integrating MCP client for enhanced user experience.